build_samples_msvc2017.bat

cpu_extension = "C:\Users\kang\Documents\Intel\OpenVINO\inference_engine_samples_build_2017\intel64\Release\cpu_extension.dll"

model_xml = ""   
model_bin = ""

import sys
import cv2
import numpy as np
import time
import logging as log
from openvino.inference_engine import IENetwork, IEPlugin

plugin_dir = "C:/Intel/openvino/deployment_tools/inference_engine/bin/intel64/Release"
cpu_extension = "C:/Users/kang/Documents/Intel/OpenVINO/inference_engine_samples_build_2017/intel64/Release/cpu_extension.dll"


# face-detection-adas-0001
model_xml  = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.xml"
model_bin = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.bin"

# emotions-recognition-retail-0003
emotions_xml = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Retail/object_attributes/emotions_recognition/0003/dldt/emotions-recognition-retail-0003.xml"
emotions_bin = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Retail/object_attributes/emotions_recognition/0003/dldt/emotions-recognition-retail-0003.bin"

labels = ['neutral', 'happy', 'sad', 'surprise', 'anger']


def face_emotions_demo():
    log.basicConfig(format="[ %(levelname)s ] %(message)s",
                    level=log.INFO,
                    stream=sys.stdout)
    # Plugin initialization for specified device and load extensions library if specified
    log.info("Initializing plugin for {} device...".format("CPU"))
    plugin = IEPlugin(device="CPU", plugin_dirs=plugin_dir)
    plugin.add_cpu_extension(cpu_extension)
    # Read IR
    log.info("Reading IR...")
    net = IENetwork(model=model_xml, weights=model_bin)


    emotions_net = IENetwork(model=emotions_xml, weights=emotions_bin)

    if plugin.device == "CPU":
        supported_layers = plugin.get_supported_layers(net)
        not_supported_layers = [
            l for l in net.layers.keys() if l not in supported_layers
        ]
        if len(not_supported_layers) != 0:
            log.error(
                "Following layers are not supported by the plugin for specified device {}:\n {}"
                .format(plugin.device, ', '.join(not_supported_layers)))
            log.error(
                "Please try to specify cpu extensions library path in demo's command line parameters using -l "
                "or --cpu_extension command line argument")
            sys.exit(1)
    assert len(
        net.inputs.keys()) == 1, "Demo supports only single input topologies"
    assert len(net.outputs) == 1, "Demo supports only single output topologies"


    input_blob = next(iter(net.inputs))
    out_blob = next(iter(net.outputs))
    em_input_blob = next(iter(emotions_net.inputs))
    em_out_blob = next(iter(emotions_net.outputs))


    log.info("Loading IR to the plugin...")

    # 生成可执行网络,异步执行 num_requests=2
    exec_net = plugin.load(network=net, num_requests=2)
    exec_emotions_net = plugin.load(network=emotions_net)

    # Read and pre-process input image
    n, c, h, w = net.inputs[input_blob].shape
    en, ec, eh, ew = emotions_net.inputs[em_input_blob].shape

    del net
    del emotions_net

    cap = cv2.VideoCapture("C:/Users/kang/Downloads/openvino_sample_show/material/face_detection_demo.mp4")

    cur_request_id = 0
    next_request_id = 1

    log.info("Starting inference in async mode...")
    log.info("To switch between sync and async modes press Tab button")
    log.info("To stop the demo execution press Esc button")
    is_async_mode = True
    render_time = 0
    ret, frame = cap.read()

    print(
        "To close the application, press 'CTRL+C' or any key with focus on the output window"
    )
    while cap.isOpened():
        if is_async_mode:
            ret, next_frame = cap.read()
        else:
            ret, frame = cap.read()
        if not ret:
            break
        initial_w = cap.get(3)
        initial_h = cap.get(4)
        inf_start = time.time()
        if is_async_mode:
            in_frame = cv2.resize(next_frame, (w, h))
            in_frame = in_frame.transpose(
                (2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            exec_net.start_async(request_id=next_request_id,
                                inputs={input_blob: in_frame})
        else:
            in_frame = cv2.resize(frame, (w, h))
            in_frame = in_frame.transpose(
                (2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            exec_net.start_async(request_id=cur_request_id,
                                inputs={input_blob: in_frame})
        if exec_net.requests[cur_request_id].wait(-1) == 0:


            res = exec_net.requests[cur_request_id].outputs[out_blob]



            # 输出格式：[1,1,N,7]  从N行人脸中找到7个值   = [image_id,label,conf,x_min,y_min,x_max,y_max]
            for obj in res[0][0]:
                if obj[2] > 0.5:
                    xmin = int(obj[3] * initial_w)
                    ymin = int(obj[4] * initial_h)
                    xmax = int(obj[5] * initial_w)
                    ymax = int(obj[6] * initial_h)
                    if xmin > 0 and ymin > 0 and (xmax < initial_w) and (ymax < initial_h):
                        roi = frame[ymin:ymax,xmin:xmax,:]
                        face_roi = cv2.resize(roi,(ew,eh))
                        face_roi =face_roi.transpose((2, 0, 1)) 
                        face_roi= face_roi.reshape((en, ec, eh, ew))
                        # 解析结果
                        landmark_res = exec_emotions_net.infer(inputs={input_blob: [face_roi]})
                        landmark_res = landmark_res['prob_emotion']
                        landmark_res = np.reshape(landmark_res, (5))
                        landmark_res = labels[np.argmax(landmark_res)]
                        cv2.putText(frame, landmark_res, (np.int32(xmin), np.int32(ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1.0,
                                (255, 0, 0), 2)
                        cv2.rectangle(frame, (np.int32(xmin), np.int32(ymin)), (np.int32(xmax), np.int32(ymax)),
                                    (0, 0, 255), 2, 8, 0)
                    cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (0, 0, 255), 2, 8, 0)


            inf_end = time.time()
            det_time = inf_end - inf_start

            # Draw performance stats
            inf_time_message = "Inference time: {:.3f} ms, FPS:{:.3f}".format(det_time * 1000, 1000 / (det_time*1000 + 1))
            render_time_message = "OpenCV rendering time: {:.3f} ms".format(
                render_time * 1000)
            async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
                "Async mode is off. Processing request {}".format(cur_request_id)

            cv2.putText(frame, inf_time_message, (15, 15),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            cv2.putText(frame, render_time_message, (15, 30),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
            cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)


        render_start = time.time()
        cv2.imshow("face emotions demo", frame)
        render_end = time.time()
        render_time = render_end - render_start

        if is_async_mode:
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame

        key = cv2.waitKey(1)
        if key == 27:
            break
    cv2.destroyAllWindows()

    del exec_net
    del exec_emotions_net
    del plugin


if __name__ == '__main__':
    sys.exit(face_emotions_demo() or 0)